Decision Tree, Bagging and Random Forest methods detect TEC seismo-ionospheric anomalies around the time of the Chile, (Mw = 8.8) earthquake of 27 February 2010

Abstract In this paper for the first time ensemble methods including Decision Tree, Bagging and Random Forest have been proposed in the field of earthquake precursors to detect GPS-TEC (Total Electron Content) seismo-ionospheric anomalies around the time and location of Chile earthquake of 27 February 2010. All of the implemented ensemble methods detected a striking anomaly in time series of TEC data, 1 day after the earthquake at 14:00 UTC. The results indicate that the proposed methods due to their performance, speed and simplicity are quite promising and deserve serious attention as a new predictor tools for seismo-ionospheric anomalies detection.

[1]  S. Pulinets,et al.  Ionospheric precursors of earthquakes , 2004 .

[2]  Xuhui Shen,et al.  ULF/ELF ionospheric electric field and plasma perturbations related to Chile earthquakes , 2011 .

[3]  S. Pulinets,et al.  Lithosphere-Atmosphere-Ionosphere Coupling (LAIC) Model - An Unified Concept for Earthquake Precursors Validation , 2011 .

[4]  M. Akhoondzadeh Thermal and TEC anomalies detection using an intelligent hybrid system around the time of the Saravan, Iran, (Mw = 7.7) earthquake of 16 April 2013 , 2014 .

[5]  Sergey Pulinets,et al.  Synchronization of atmospheric indicators at the last stage of earthquake preparation cycle , 2015 .

[6]  M. Akhoondzadeh,et al.  A MLP neural network as an investigator of TEC time series to detect seismo-ionospheric anomalies , 2013 .

[7]  M. Bravo,et al.  Maximum electron concentration and total electron content of the ionosphere over Concepción, Chile, prior to the 27 February 2010 earthquake , 2013 .

[8]  M. Akhoondzadeh Anomalous TEC variations associated with the powerful Tohoku earthquake of 11 March 2011 , 2012 .

[9]  Yi-Ben Tsai,et al.  Pre-earthquake ionospheric anomalies registered by continuous GPS TEC measurements , 2004 .

[10]  Matthew Scotch,et al.  Comparison of ARIMA and Random Forest time series models for prediction of avian influenza H5N1 outbreaks , 2014, BMC Bioinformatics.

[11]  Z. Qiang,et al.  THERMAL INFRARED ANOMALY PRECURSOR OF IMPENDING EARTHQUAKES , 1991 .

[12]  早川 正士,et al.  Seismo electromagnetics : lithosphere-atmosphere-ionosphere coupling , 2002 .

[13]  M. Akhoondzadeh Support vector machines for TEC seismo-ionospheric anomalies detection , 2013 .

[14]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[15]  M. Akhoondzadeh,et al.  An Adaptive Network-based Fuzzy Inference System for the detection of thermal and TEC anomalies around the time of the Varzeghan, Iran, (Mw = 6.4) earthquake of 11 August 2012 , 2013 .

[16]  M. Parrot Use of satellites to detect seismo-electromagnetic effects , 1995 .

[17]  Jann‐Yenq Liu,et al.  Seismo-ionospheric anomalies in total electron content of the GIM and electron density of DEMETER before the 27 February 2010 M8.8 Chile earthquake , 2013 .

[18]  M. Akhoondzadeh,et al.  Genetic algorithm for TEC seismo-ionospheric anomalies detection around the time of the Solomon (Mw = 8.0) earthquake of 06 February 2013 , 2013 .